IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v75y2025ics154461232500131x.html
   My bibliography  Save this article

Can deep reinforcement learning beat 1N

Author

Listed:
  • Kruthof, Garvin
  • Müller, Sebastian

Abstract

Deep reinforcement learning (DRL) has emerged as a promising tool for portfolio management. However, limited datasets in prior research hinder the generalizability of findings. We conduct a large-scale evaluation of the Soft Actor-Critic (SAC) algorithm across seven diverse equity datasets, spanning over 300 years of out-of-sample data. While SAC demonstrates market timing potential, it does not systematically outperform a 1N benchmark in a frictionless setting. SAC’s high turnover leads to negative net returns under modest transaction costs (0.1%), whereas the 1N strategy and alternative lower-turnover strategies remain robust. Distribution-based and tail-risk measures do not reveal a consistent advantage for SAC. Our results highlight the practical challenges faced by high-frequency DRL strategies and emphasize the need for future research on cost-aware DRL methods and robust validation protocols.

Suggested Citation

  • Kruthof, Garvin & Müller, Sebastian, 2025. "Can deep reinforcement learning beat 1N," Finance Research Letters, Elsevier, vol. 75(C).
    • Handle: RePEc:eee:finlet:v:75:y:2025:i:c:s154461232500131x
    DOI: 10.1016/j.frl.2025.106866
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S154461232500131X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2025.106866?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jacobs, Heiko & Müller, Sebastian & Weber, Martin, 2014. "How should individual investors diversify? An empirical evaluation of alternative asset allocation policies," Journal of Financial Markets, Elsevier, vol. 19(C), pages 62-85.
    2. Jinho Lee & Raehyun Kim & Yookyung Koh & Jaewoo Kang, 2019. "Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network," Papers 1902.10948, arXiv.org.
    3. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    4. Ravi Jagannathan & Tongshu Ma, 2003. "Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps," Journal of Finance, American Finance Association, vol. 58(4), pages 1651-1683, August.
    5. Fletcher, Jonathan, 2011. "Do optimal diversification strategies outperform the 1/N strategy in U.K. stock returns?," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 375-385.
    6. Victor DeMiguel & Lorenzo Garlappi & Francisco J. Nogales & Raman Uppal, 2009. "A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms," Management Science, INFORMS, vol. 55(5), pages 798-812, May.
    7. Cui, Tianxiang & Du, Nanjiang & Yang, Xiaoying & Ding, Shusheng, 2024. "Multi-period portfolio optimization using a deep reinforcement learning hyper-heuristic approach," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    8. Francesco Cesarone & Raffaello Cesetti & Giuseppe Orlando & Manuel Luis Martino & Jacopo Maria Ricci, 2022. "Comparing SSD-Efficient Portfolios with a Skewed Reference Distribution," Mathematics, MDPI, vol. 11(1), pages 1-20, December.
    9. Zhipeng Liang & Hao Chen & Junhao Zhu & Kangkang Jiang & Yanran Li, 2018. "Adversarial Deep Reinforcement Learning in Portfolio Management," Papers 1808.09940, arXiv.org, revised Nov 2018.
    10. Andrew Y. Chen & Tom Zimmermann, 2022. "Open Source Cross-Sectional Asset Pricing," Critical Finance Review, now publishers, vol. 11(2), pages 207-264, May.
    11. Nicolò Giunta & Giuseppe Orlando & Alessandra Carleo & Jacopo Maria Ricci, 2024. "Exploring Entropy-Based Portfolio Strategies: Empirical Analysis and Cryptocurrency Impact," Risks, MDPI, vol. 12(5), pages 1-26, May.
    12. Ledoit, Oliver & Wolf, Michael, 2008. "Robust performance hypothesis testing with the Sharpe ratio," Journal of Empirical Finance, Elsevier, vol. 15(5), pages 850-859, December.
    13. Mark Kritzman & Sébastien Page & David Turkington, 2010. "In Defense of Optimization: The Fallacy of 1/N," Financial Analysts Journal, Taylor & Francis Journals, vol. 66(2), pages 31-39, March.
    14. Stambaugh, Robert F. & Yu, Jianfeng & Yuan, Yu, 2012. "The short of it: Investor sentiment and anomalies," Journal of Financial Economics, Elsevier, vol. 104(2), pages 288-302.
    15. repec:bla:jfinan:v:58:y:2003:i:4:p:1651-1684 is not listed on IDEAS
    16. Jobson, J D & Korkie, Bob M, 1981. "Performance Hypothesis Testing with the Sharpe and Treynor Measures," Journal of Finance, American Finance Association, vol. 36(4), pages 889-908, September.
    17. Bekiros, Stelios D., 2010. "Heterogeneous trading strategies with adaptive fuzzy Actor-Critic reinforcement learning: A behavioral approach," Journal of Economic Dynamics and Control, Elsevier, vol. 34(6), pages 1153-1170, June.
    18. Angelos Filos, 2019. "Reinforcement Learning for Portfolio Management," Papers 1909.09571, arXiv.org.
    19. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
    20. Fischer, Thomas G., 2018. "Reinforcement learning in financial markets - a survey," FAU Discussion Papers in Economics 12/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Candelon, B. & Hurlin, C. & Tokpavi, S., 2012. "Sampling error and double shrinkage estimation of minimum variance portfolios," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 511-527.
    2. McDowell, Shaun, 2018. "An empirical evaluation of estimation error reduction strategies applied to international diversification," Journal of Multinational Financial Management, Elsevier, vol. 44(C), pages 1-13.
    3. Jacobs, Heiko & Müller, Sebastian & Weber, Martin, 2014. "How should individual investors diversify? An empirical evaluation of alternative asset allocation policies," Journal of Financial Markets, Elsevier, vol. 19(C), pages 62-85.
    4. Michele Costola & Bertrand Maillet & Zhining Yuan & Xiang Zhang, 2024. "Mean–variance efficient large portfolios: a simple machine learning heuristic technique based on the two-fund separation theorem," Annals of Operations Research, Springer, vol. 334(1), pages 133-155, March.
    5. Gelmini, Matteo & Uberti, Pierpaolo, 2024. "The equally weighted portfolio still remains a challenging benchmark," International Economics, Elsevier, vol. 179(C).
    6. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Papers 2107.13866, arXiv.org.
    7. Sven Husmann & Antoniya Shivarova & Rick Steinert, 2022. "Sparsity and stability for minimum-variance portfolios," Risk Management, Palgrave Macmillan, vol. 24(3), pages 214-235, September.
    8. Cheng Yan & Ji Yan, 2021. "Optimal and naive diversification in an emerging market: Evidence from China's A‐shares market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3740-3758, July.
    9. Max Heide & Benjamin R. Auer & Frank Schuhmacher, 2025. "Optimal Versus Naive Diversification in Commodity Futures Markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(1), pages 3-22, January.
    10. Gossé, Jean-Baptiste & Jehle, Camille, 2024. "Benefits of diversification in EU capital markets: Evidence from stock portfolios," Economic Modelling, Elsevier, vol. 135(C).
    11. Sven Husmann & Antoniya Shivarova & Rick Steinert, 2019. "Sparsity and Stability for Minimum-Variance Portfolios," Papers 1910.11840, arXiv.org.
    12. McDowell, Shaun, 2018. "The benefits of international diversification with weight constraints: A cross-country examination," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 99-109.
    13. Stadtmüller, Immo & Auer, Benjamin R. & Schuhmacher, Frank, 2022. "On the benefits of active stock selection strategies for diversified investors," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 342-354.
    14. Liusha Yang & Romain Couillet & Matthew R. McKay, 2015. "A Robust Statistics Approach to Minimum Variance Portfolio Optimization," Papers 1503.08013, arXiv.org.
    15. Seyoung Park & Eun Ryung Lee & Sungchul Lee & Geonwoo Kim, 2019. "Dantzig Type Optimization Method with Applications to Portfolio Selection," Sustainability, MDPI, vol. 11(11), pages 1-32, June.
    16. Cederburg, Scott & O’Doherty, Michael S. & Wang, Feifei & Yan, Xuemin (Sterling), 2020. "On the performance of volatility-managed portfolios," Journal of Financial Economics, Elsevier, vol. 138(1), pages 95-117.
    17. Maillet, Bertrand & Tokpavi, Sessi & Vaucher, Benoit, 2015. "Global minimum variance portfolio optimisation under some model risk: A robust regression-based approach," European Journal of Operational Research, Elsevier, vol. 244(1), pages 289-299.
    18. Ammann, Manuel & Coqueret, Guillaume & Schade, Jan-Philip, 2016. "Characteristics-based portfolio choice with leverage constraints," Journal of Banking & Finance, Elsevier, vol. 70(C), pages 23-37.
    19. Karstanje, Dennis & Sojli, Elvira & Tham, Wing Wah & van der Wel, Michel, 2013. "Economic valuation of liquidity timing," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5073-5087.
    20. Füss, Roland & Miebs, Felix & Trübenbach, Fabian, 2014. "A jackknife-type estimator for portfolio revision," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 14-28.

    More about this item

    Keywords

    Deep reinforcement learning; Portfolio optimization; Diversification; Portfolio management; 1/N;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:75:y:2025:i:c:s154461232500131x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.